Getting Started with TensorFlow

Get up and running with the latest numerical computing library by Google and dive deeper into your data!

Getting Started with TensorFlow

This ebook is included in a Mapt subscription
Giancarlo Zaccone

9 customer reviews
Get up and running with the latest numerical computing library by Google and dive deeper into your data!
$10.00
$34.99
RRP $27.99
RRP $34.99
eBook
Print + eBook
Preview in Mapt

Book Details

ISBN 139781786468574
Paperback180 pages

Book Description

Google's TensorFlow engine, after much fanfare, has evolved in to a robust, user-friendly, and customizable, application-grade software library of machine learning (ML) code for numerical computation and neural networks.

This book takes you through the practical software implementation of various machine learning techniques with TensorFlow. In the first few chapters, you'll gain familiarity with the framework and perform the mathematical operations required for data analysis. As you progress further, you'll learn to implement various machine learning techniques such as classification, clustering, neural networks, and deep learning through practical examples.

By the end of this book, you’ll have gained hands-on experience of using TensorFlow and building classification, image recognition systems, language processing, and information retrieving systems for your application.

Table of Contents

Chapter 1: TensorFlow – Basic Concepts
Machine learning and deep learning basics
TensorFlow – A general overview
Python basics
Installing TensorFlow
First working session
Data Flow Graphs
TensorFlow programming model
Summary
Chapter 2: Doing Math with TensorFlow
The tensor data structure
Complex numbers and fractals
Computing gradients
Random numbers
Solving partial differential equations
Summary
Chapter 3: Starting with Machine Learning
The linear regression algorithm
The MNIST dataset
Classifiers
Data clustering
Summary
Chapter 4: Introducing Neural Networks
What are artificial neural networks?
Single Layer Perceptron
The logistic regression
Multi Layer Perceptron
Summary
Chapter 5: Deep Learning
Deep learning techniques
Summary
Chapter 6: GPU Programming and Serving with TensorFlow
GPU programming
TensorFlow Serving
Loading and exporting a TensorFlow model
Summary

What You Will Learn

  • Install and adopt TensorFlow in your Python environment to solve mathematical problems
  • Get to know the basic machine and deep learning concepts
  • Train and test neural networks to fit your data model
  • Make predictions using regression algorithms
  • Analyze your data with a clustering procedure
  • Develop algorithms for clustering and data classification
  • Use GPU computing to analyze big data

Authors

Table of Contents

Chapter 1: TensorFlow – Basic Concepts
Machine learning and deep learning basics
TensorFlow – A general overview
Python basics
Installing TensorFlow
First working session
Data Flow Graphs
TensorFlow programming model
Summary
Chapter 2: Doing Math with TensorFlow
The tensor data structure
Complex numbers and fractals
Computing gradients
Random numbers
Solving partial differential equations
Summary
Chapter 3: Starting with Machine Learning
The linear regression algorithm
The MNIST dataset
Classifiers
Data clustering
Summary
Chapter 4: Introducing Neural Networks
What are artificial neural networks?
Single Layer Perceptron
The logistic regression
Multi Layer Perceptron
Summary
Chapter 5: Deep Learning
Deep learning techniques
Summary
Chapter 6: GPU Programming and Serving with TensorFlow
GPU programming
TensorFlow Serving
Loading and exporting a TensorFlow model
Summary

Book Details

ISBN 139781786468574
Paperback180 pages
Read More
From 9 reviews

Read More Reviews